Agents and Intelligent Systems

We are interested in building intelligent systems that mimic human behavior. Let's study the key elements for bringing such systems to life.

Agents

An agent is a program or machine that can sense what's happening around it and make decisions to do something useful. It tries to achieve a goal by sensing its surroundings and then acting in a way that will get it closer to that goal. In other words, an agent can sense the environment using sensors and act accordingly through actuators.

The table below shows some interesting examples of agents with sensors and actuators.

Agent function

Agents receive input through sensors; we call this input "percept." A series of inputs perceived by an agent through its sensors is called percept history. Typically, an agent's decision on which action to take at any moment, using its actuators, can be based on the entire percept sequence up to that point. If we can define the agent's actions for every possible percept sequence, we have essentially described the agent's behavior completely. Mathematically, an agent's behavior is represented by the agent function, which maps each percept sequence to a corresponding action that the agent performs using its actuators.

Example: Light control agent

Let's understand all these concepts with the aid of an example.

Here’s a simple table for our automated light control system:

As you add more percept variables or increase the number of possible values for each variable, the number of possible percept combinations grows exponentially, which in turn increases the complexity of defining the agent function. 

Mathematical representation:

Here is the agent function designed specifically for this scenario:

Given a percept $p = (L, M)$ ...